Skip to main content
Glama
Sign In
AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Analyze Relationship

analyze_relationship

Compute the correlation between two numerical columns in a dataset to assess their relationship. Returns the dataset ID, column names, and correlation value when calculable.

Instructions

Analyze the correlation between two numerical columns. Args: dataset_id (str): ID of the dataset. col_name1 (str): First numerical column. col_name2 (str): Second numerical column. Returns: dict: Dataset ID, column names, and correlation value (None if it cannot be computed).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>",
      "col_name1": "<col_name1>",
      "col_name2": "<col_name2>"
    },
    "name": "analyze_relationship"
  }
}

Input Schema

NameRequiredDescriptionDefault
col_name1Yes
col_name2Yes
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full burden. It does disclose the return shape and that the correlation may be None when it cannot be computed, which is useful grace-failure context. But it never names the correlation method (Pearson, Spearman, etc.), how non-numeric or missing data is handled, or whether it errors or returns None in those cases — significant gaps for a statistics tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded one-line purpose followed by compact Args/Returns blocks — well structured and free of filler. The docstring style is slightly verbose relative to the tiny schema but every line carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a three-parameter analytical tool with no annotations and no output schema, the definition should say more about method selection and failure behavior. Returning None 'if it cannot be computed' hints at edge cases without explaining them, which leaves an agent guessing about invalid inputs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate, and it does list all three parameters with meaning: dataset_id, first numerical column, second numerical column. That covers every parameter, but adds no detail on ordering effects, same-column input, or expected naming/format conventions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Analyze the correlation between two numerical columns.' An agent knows exactly the computation performed. However, it does not differentiate itself from the sibling analyze_correlation, leaving the pair indistinguishable from the description alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance whatsoever on when to choose this over analyze_correlation, analyze_distribution, or other analysis siblings. It also doesn't state prerequisites such as both columns needing to exist in the given dataset and be numeric.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools